Incomplete views are a common problem in multi-view clustering [1]}; we cannot expect all images to be equipped with text descriptions. To analyze the robustness of JECL when text descriptions are missing, we remove text from a random set of images at varying rates on Coco-cross and all-sub dataset. We compare our method against two state-of-the-art incomplete multi-view clustering model, PIC [2]} and DAIMC [3]}. The missing view experimental results appear in Figure REF . JECL is competitive with PIC on both datasets and outperforms DAIMC by a large margin. Figure REF  demonstrates that images with captions (orange dots) have larger value of \(p_{ij}\)  and contribute a larger gradient to the training process. Images with missing text have a smaller associated value of \(p_{ij}\)  because the second term in equation (REF ) vanishes.
